W. Gan, “Computational Imaging Under Incomplete Information,” Ph.D. dissertation, McKelvey School of Engineering, Washington Univ. in St. Louis, St. Louis, MO, USA, Thesis no. 1284, 2025. [10.7936/0jqg-3c40]
N. Asokan, “Adaptive Noise Estimation and Denoising with Deep Learning for NMR Spectroscopy,” M.S. thesis, McKelvey School of Engineering, Washington Univ. in St. Louis, St. Louis, MO, USA, Thesis no. 1193, 2025. [10.7936/mwe1-cg78]
H. Zhang, “Learning-Based MRI Reconstruction Method with Coil Sensitivity Estimation and Prior Adaptation,” M.S. thesis, McKelvey School of Engineering, Washington Univ. in St. Louis, St. Louis, MO, USA, Thesis no. 1195, 2025. [10.7936/a8gw-7d93]
2024
J. Liu, “Learning-Based Artifacts Removal Priors for Computational Imaging,” Ph.D. dissertation, McKelvey School of Engineering, Washington Univ. in St. Louis, St. Louis, MO, USA, Thesis no. 1072, 2024. [10.7936/b39z-1716]
2022
Y. Sun, “Integrating Physical Models and Deep Priors for Computational Imaging,” Ph.D. dissertation, McKelvey School of Engineering, Washington Univ. in St. Louis, St. Louis, MO, USA, Thesis no. 802, 2022. [10.7936/8hk3-b391]
X. Xu, “Model-Based Deep Learning for Computational Imaging,” Ph.D. dissertation, McKelvey School of Engineering, Washington Univ. in St. Louis, St. Louis, MO, USA, Thesis no. 808, 2022. [10.7936/r9e7-ng63]
Y. Hu, “Self-Supervised Joint Image Reconstruction and Coil Sensitivity Calibration in Parallel MRI Without Ground Truth,” M.S. thesis, McKelvey School of Engineering, Washington Univ. in St. Louis, St. Louis, MO, USA, Thesis no. 713, 2022. [10.7936/5qsm-fy77]
H. James, “Investigating Applications of Deep Learning for Diagnosis of Post Traumatic Elbow Disease,” M.S. thesis, McKelvey School of Engineering, Washington Univ. in St. Louis, St. Louis, MO, USA, Thesis no. 762, 2022. [10.7936/e82q-1e65]
J. Liu, R. Hyder, S. Asif, and U. S. Kamilov, “Optimization Algorithms for MR Reconstruction,” in Magnetic Resonance Image Reconstruction, M. Akcakaya, M. Doneva, C. Prieto, Eds. Elsevier, 2022, ch. 3, pp. 59–72. [isbn:978-0-12-822726-8]
2021
R. Liu, “Regularized Coordinate-Based Neural Representation Learning for Optical Tomography,” M.S. thesis, McKelvey School of Engineering, Washington Univ. in St. Louis, St. Louis, MO, USA, Thesis no. 686, 2021. [10.7936/3v1p-pb86]
W. Shangguan, “NeVR: Learning Continuous Neural Video Representation with Local Feature Codes for Video Interpolation,” M.S. thesis, McKelvey School of Engineering, Washington Univ. in St. Louis, St. Louis, MO, USA, Thesis no. 681, 2021. [10.7936/r51t-mq19]
2020
W. Gan, “Toward Practical Learning-Based Image Reconstruction Methods,” M.S. thesis, McKelvey School of Engineering, Washington Univ. in St. Louis, St. Louis, MO, USA, Thesis no. 518, 2020. [10.7936/emc4-jj77]
2019
S. Xu, “Regularized Fourier Ptychographic Microscopy,” M.S. thesis, McKelvey School of Engineering, Washington Univ. in St. Louis, St. Louis, MO, USA, Thesis no. 697, 2019. [10.7936/0k2s-5378]
2015
U. S. Kamilov, “Sparsity-Driven Statistical Inference for Inverse Problems,” Swiss Federal Institute of Technology Lausanne, EPFL Thesis no. 6545 (2015), 198 p., March 27, 2015. [10.5075/epfl-thesis-6545]